Learning environments in higher education: Their adaptability to the 4th industrial revolution and the 'social transformation' discourse

The South African higher-education sector is currently undergoing a significant phase in its transition. The phase is marked by a sense of uncertainty felt across institutions and entities that make up the sector. This uncertainty, to a large extent, is brought about by the socio-political realities the transition entails. Compounding this situation is the advent of the 4th Industrial Revolution (Hadden), a phenomenon to which the higher-education sector needs a heightened degree of adaptability. The learning environments provided by the higher-education sector are therefore crucial in terms of advancing the cause of positive social change as a realisable educational objective. Against this backdrop, this conceptual article examines the issue of social change as a moral imperative. The purpose is therefore to contribute to the 4IR discourse currently evolving in the context of South African higher education and its social change agenda, with cognitive capitalism as a theoretical lens. Significant scholarly work has been done on the issue of technological advancement and its implications for the social practice of education. However, a concerted effort has not been undertaken to examine the 4IR as an inevitable educational experience with potential to be both materialistically transformative and morally enslaving. The article concludes that, as 4IR unfolds into a magnificent event and starts to control every aspect of human life in general, and education in particular, the moral and ethical affirmations that support the experience of education may run into troubled waters

"Critical statistical literacy", "Social justice statistics", and "Critical statistical consciousness" as higher education imperatives, amidst the COVID-19 pandemic in South Africa

Familiarity with the Covid-19 pandemic-related statistical jargon is a requirement for the layman, who must stay abreast of developments apropos of rate of infection, spread, and mortality rate comprising the pandemic. Such a familiarity, termed statistical literacy (SL) in the related discourse, is increasingly becoming an important aspect of higher education (HE) studentship across universities, internationally. This article offers an extension to the extant theorisations of statistical literacy in the context of Covid-19. Formulation of a solid theoretical rationale for fostering the competency of SL at the HE level is therefore central to this article. The literature offers the notion of critical statistical literacy (CSL) as anchored in a social-justice paradigm. CSL is used here as a starting point for the theoretical extensions proposed in this article. A novel disciplinary idea called “social justice statistics (SJS)” is also introduced. The idea of “critical statistical consciousness (CSC)” as a new proposition for theorising the statistical sensibility of citizens is also put forward. The ways in which CSC rationalises CSL, and foregrounds SJS, are subsequently theorised. CSC, as a broad attribute, quality, or a higher-education trait, is thus positioned in the context of the Covid-19 pandemic. These theorisations have implications for the practice of statistics, both at the levels of “producer” and “consumer” of statistical communications that characterise the way in which the pandemic is understood by the layman

Detecting faults in process systems with singular spectrum analysis

In this study, process monitoring based on signal decomposition by use of singular spectrum analysis (SSA) is considered. SSA makes use of adaptive basis functions to decompose a time series into multiple components that may be periodic, aperiodic or random. Two variants of SSA are considered in this investigation. In the first, the conventional approach is used based on latent variables extracted from the covariances of the lagged trajectory matrix of the process variables. The second approach is identical to the first approach, except that the covariances of the lagged trajectory matrices are replaced by Euclidean distance dissimilarities to decompose the variables into additive components. These components are subsequently monitored and the merits of the two approaches are considered on the basis of two case studies using simulated nonlinear data and data from the benchmark Tennessee Eastman process